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Create TARGETS for captum/attr (#1263)
Summary: Split captum/attr a separate TARGETS Differential Revision: D55293289
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,39 @@ | ||
| # (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary. | ||
| from typing import Tuple, Union | ||
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| import torch | ||
| from captum._utils.typing import Tensor | ||
| from tests.helpers import BaseTest | ||
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| def assert_attribution_delta( | ||
| test: BaseTest, | ||
| inputs: Union[Tensor, Tuple[Tensor, ...]], | ||
| attributions: Union[Tensor, Tuple[Tensor, ...]], | ||
| n_samples: int, | ||
| delta: Tensor, | ||
| delta_thresh: Union[float, Tensor] = 0.0006, | ||
| is_layer: bool = False, | ||
| ) -> None: | ||
| if not is_layer: | ||
| for input, attribution in zip(inputs, attributions): | ||
| test.assertEqual(attribution.shape, input.shape) | ||
| if isinstance(inputs, tuple): | ||
| bsz = inputs[0].shape[0] | ||
| else: | ||
| bsz = inputs.shape[0] | ||
| test.assertEqual([bsz * n_samples], list(delta.shape)) | ||
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| delta = torch.mean(delta.reshape(bsz, -1), dim=1) | ||
| assert_delta(test, delta, delta_thresh) | ||
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| def assert_delta( | ||
| test: BaseTest, delta: Tensor, delta_thresh: Union[Tensor, float] = 0.0006 | ||
| ) -> None: | ||
| delta_condition = (delta.abs() < delta_thresh).all() | ||
| test.assertTrue( | ||
| delta_condition, | ||
| "Sum of SHAP values {} does" | ||
| " not match the difference of endpoints.".format(delta), | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,49 @@ | ||
| # (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary. | ||
| from typing import Any, Tuple | ||
|
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| import torch | ||
| from captum._utils.gradient import compute_gradients | ||
| from tests.helpers.basic_models import BasicModel, BasicModel5_MultiArgs | ||
| from torch import Tensor | ||
| from torch.nn import Module | ||
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| def get_basic_config() -> Tuple[Module, Tensor, Tensor, Any]: | ||
| input = torch.tensor([1.0, 2.0, 3.0, 0.0, -1.0, 7.0], requires_grad=True).T | ||
| # manually percomputed gradients | ||
| grads = torch.tensor([-0.0, -0.0, -0.0, 1.0, 1.0, -0.0]) | ||
| return BasicModel(), input, grads, None | ||
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| def get_multiargs_basic_config() -> ( | ||
| Tuple[Module, Tuple[Tensor, ...], Tuple[Tensor, ...], Any] | ||
| ): | ||
| model = BasicModel5_MultiArgs() | ||
| additional_forward_args = ([2, 3], 1) | ||
| inputs = ( | ||
| torch.tensor([[1.5, 2.0, 34.3], [3.4, 1.2, 2.0]], requires_grad=True), | ||
| torch.tensor([[3.0, 3.5, 23.2], [2.3, 1.2, 0.3]], requires_grad=True), | ||
| ) | ||
| grads = compute_gradients( | ||
| model, inputs, additional_forward_args=additional_forward_args | ||
| ) | ||
| return model, inputs, grads, additional_forward_args | ||
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| def get_multiargs_basic_config_large() -> ( | ||
| Tuple[Module, Tuple[Tensor, ...], Tuple[Tensor, ...], Any] | ||
| ): | ||
| model = BasicModel5_MultiArgs() | ||
| additional_forward_args = ([2, 3], 1) | ||
| inputs = ( | ||
| torch.tensor( | ||
| [[10.5, 12.0, 34.3], [43.4, 51.2, 32.0]], requires_grad=True | ||
| ).repeat_interleave(3, dim=0), | ||
| torch.tensor( | ||
| [[1.0, 3.5, 23.2], [2.3, 1.2, 0.3]], requires_grad=True | ||
| ).repeat_interleave(3, dim=0), | ||
| ) | ||
| grads = compute_gradients( | ||
| model, inputs, additional_forward_args=additional_forward_args | ||
| ) | ||
| return model, inputs, grads, additional_forward_args |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,39 @@ | ||
| # (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary. | ||
| from typing import Tuple | ||
|
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| import torch | ||
| from torch import Tensor | ||
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| def create_inps_and_base_for_deeplift_neuron_layer_testing() -> ( | ||
| Tuple[Tuple[Tensor, Tensor], Tuple[Tensor, Tensor]] | ||
| ): | ||
| x1 = torch.tensor([[-10.0, 1.0, -5.0]], requires_grad=True) | ||
| x2 = torch.tensor([[3.0, 3.0, 1.0]], requires_grad=True) | ||
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| b1 = torch.tensor([[0.0, 0.0, 0.0]], requires_grad=True) | ||
| b2 = torch.tensor([[0.0, 0.0, 0.0]], requires_grad=True) | ||
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| inputs = (x1, x2) | ||
| baselines = (b1, b2) | ||
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| return inputs, baselines | ||
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| def create_inps_and_base_for_deepliftshap_neuron_layer_testing() -> ( | ||
| Tuple[Tuple[Tensor, Tensor], Tuple[Tensor, Tensor]] | ||
| ): | ||
| x1 = torch.tensor([[-10.0, 1.0, -5.0]], requires_grad=True) | ||
| x2 = torch.tensor([[3.0, 3.0, 1.0]], requires_grad=True) | ||
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| b1 = torch.tensor( | ||
| [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], requires_grad=True | ||
| ) | ||
| b2 = torch.tensor( | ||
| [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], requires_grad=True | ||
| ) | ||
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| inputs = (x1, x2) | ||
| baselines = (b1, b2) | ||
|
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| return inputs, baselines |
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